Using high-resolution satellite imagery, Peter Fretwell and his team were able to achieve an unprecedented feat and, in 2009, created the world’s first census of species taken from space. Then went on to repeat it. “We started with penguins then have gone on to look at whales, then seals – we haven’t published much on seals yet. Now, we are working on albatross.” – said Fretwell.

Fretwell worked in the Antarctic for 15 years, initially as a cartographer, mapping the area for pilots. He accidentally found himself one of the world’s leading expert on animal population monitoring from satellite data while trying to locate bird colonies in Antarctica with greater accuracy. “Pilots need to know where the runways are, where the mountains are, but also, where the bird colonies are, in order to avoid them.” His team found the emperor penguins were poorly geo-referenced, as they breed in extremely remote places and move around a lot.

“I was looking for one specific colony that I thought was near Halley station but our knowledge wasn’t good enough for the mapping necessary. While remapping the coast line, I remember noticing a small brown stain on the sea line in the Landsat imagery, which turned out to be the guano from the penguin colony.”

From there, using open satellite data, Fretwell and his team at the British Antarctic Survey were able to locate previous unknown colonies. In 2012 using the same methodology, the team mapped penguin huddles – which can contains hundreds of birds – then looked to obtain higher resolution satellite imagery for the areas where colonies were located. The remotely-sensed images were then analysed using a supervised classification method to separate penguins from snow, shadows, and guano and actual counts of penguins from eleven ground truthing sites were used to convert these classified areas into numbers of penguins using a robust regression algorithm. Peter Fretwell and colleague Michelle LaRue estimated the population size to be around 595,000 penguins – twice as much as previously thought.

Continuing his work, Fretwell moved on to whale counting. In 2014, Fretwell and his team came up with an algorithm to automatically identify ’whale-like’ features in the ocean. The early version of the automated approach found 89% of probable whales identified in the manual count.

Many whale species are currently endangered. Knowing more about their behaviours and population size would help protect them. However, previous counts were conducted from a shore position, from the deck of a ship, or from a plane, rendering them necessarily narrow in scope. Additionally, for many species breeding grounds are often within the 200-nautical mile radius of coastal waters, making it difficult to track, in part due to political or military restrictions.

With the help of the International Whale Commission, the team decided to focus on 4 whale species: fin whales, southern right whales, humpback whales, and grey whales and analyze how difficult each specie would be to map.

“Whales are quite difficult [to identify via satellites]. Most things stay the same shape but whales may be on the surface, breaking the surface, underneath the surface. You may see parts of it but not all of it – you might see the head and tail but not the flippers.”

The acrobatic humpback whale, often active around whale-watching boats, splashing around and twisting into different shapes, is much harder to identify automatically. Fin whales however, who are hard to see from boats, as they rarely surface, do spend a lot of their time a few meters below the surface in a nice, calm behaviour – which makes them a great target for a satellite census.

Discriminating whales from non-whale objects in Earth observation data aren’t the only issues the team faced. “Many of the places we are looking at have high cloud cover or a rough sea, so the satellite needs to take quite a few images before it gets a good one.” Additionally, the team relies on change detection methods, which also require the satellite to record several images. “Wildlife would move whereas a rock doesn’t. You can distinguish a polar bear from a rock by seeing which one moves and which one doesn’t.” It was then crucial to find areas that were heavily covered by satellites.

Finally, when looking for huddles of penguins or guano stains, a coarser image resolution would do but for whale counting or albatross population monitoring, it was necessary to obtain sub-meter resolution satellite images. In this instance, Fretwell and team have been using WorldView-3, a DigitalGlobe satellite, which provides images up to 30 centimeters.

Despite all of these hurdles, the results are there: using imagery taken on clear days over 5,000 square kilometers of the pelagic zone of the Mediterranean sea – the main foraging grounds for fin whales in the area – and statistical regression based on surfacing estimates for the species – the team managed to get a fairly close estimate. Enough to validate their efforts and bring hope to all the whale lovers around the world.